Blockchain Technology: Investing in a National EMR Strategy
Bibliographic record
Abstract
Improving the efficiency and sustainability of Canada’s healthcare system is currently being prioritized by the federal government. In order to achieve this, government and non-government stakeholder collaboration will be required to improve integration, universality, and equity for all populations throughout the country. Although technological innovations pose certain risks, investing in health technologies plays a major role in improving service delivery and cost-saving within healthcare. Canada has historically had a relatively low use of electronic medical records (EMR’s) in comparison to other high-income countries - which appears to be partially due to inefficiencies within the fragmented systems throughout Canada. Improving the current EMR infrastructure has the potential to save our country substantial amounts of money, improve information transfer for patients and practitioners, and enhance the overall quality of medical care Canadian citizens receive. There has been mention of developing a national EMR strategy by various organizations, including the Canadian Medical Association. Blockchain technology appears to have many desired characteristics for developing a comprehensive national EMR strategy to support the needs of our universal healthcare system. This is due to the fact that it is a secure platform to store huge amounts of information and make data transfers between a diverse group of stakeholders. It is believed that blockchain can provide a unique framework upon which a national data system can be built.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".